GitHub - ranaroussi/quantstats: Portfolio analytics for quants, written in Pytho...
source link: https://github.com/ranaroussi/quantstats
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QuantStats: Portfolio analytics for quants
QuantStats Python library that performs portfolio profiling, allowing quants and portfolio managers to understand their performance better by providing them with in-depth analytics and risk metrics.
QuantStats is comprised of 3 main modules:
quantstats.stats
- for calculating various performance metrics, like Sharpe ratio, Win rate, Volatility, etc.quantstats.plots
- for visualizing performance, drawdowns, rolling statistics, monthly returns, etc.quantstats.reports
- for generating metrics reports, batch plotting, and creating tear sheets that can be saved as an HTML file.
Here's an example of a simple tear sheet analyzing a strategy:
Quick Start
%matplotlib inline import quantstats as qs # extend pandas functionality with metrics, etc. qs.extend_pandas() # fetch the daily returns for a stock stock = qs.utils.download_returns('FB') # show sharpe ratio qs.stats.sharpe(stock) # or using extend_pandas() :) stock.sharpe()
Output:
0.8135304438803402
Visualize stock performance
qs.plots.snapshot(stock, title='Facebook Performance') # can also be called via: # stock.plot_snapshot(title='Facebook Performance')
Output:
Creating a report
You can create 7 different report tearsheets:
qs.reports.metrics(mode='basic|full", ...)
- shows basic/full metricsqs.reports.plots(mode='basic|full", ...)
- shows basic/full plotsqs.reports.basic(...)
- shows basic metrics and plotsqs.reports.full(...)
- shows full metrics and plotsqs.reports.html(...)
- generates a complete report as html
Let' create an html tearsheet
(benchmark can be a pandas Series or ticker) qs.reports.html(stock, "SPY")
Output will generate something like this:
To view a complete list of available methods, run
[f for f in dir(qs.stats) if f[0] != '_']
['avg_loss', 'avg_return', 'avg_win', 'best', 'cagr', 'calmar', 'common_sense_ratio', 'comp', 'compare', 'compsum', 'conditional_value_at_risk', 'consecutive_losses', 'consecutive_wins', 'cpc_index', 'cvar', 'drawdown_details', 'expected_return', 'expected_shortfall', 'exposure', 'gain_to_pain_ratio', 'geometric_mean', 'ghpr', 'greeks', 'implied_volatility', 'information_ratio', 'kelly_criterion', 'kurtosis', 'max_drawdown', 'monthly_returns', 'outlier_loss_ratio', 'outlier_win_ratio', 'outliers', 'payoff_ratio', 'profit_factor', 'profit_ratio', 'r2', 'r_squared', 'rar', 'recovery_factor', 'remove_outliers', 'risk_of_ruin', 'risk_return_ratio', 'rolling_greeks', 'ror', 'sharpe', 'skew', 'sortino', 'adjusted_sortino', 'tail_ratio', 'to_drawdown_series', 'ulcer_index', 'ulcer_performance_index', 'upi', 'utils', 'value_at_risk', 'var', 'volatility', 'win_loss_ratio', 'win_rate', 'worst']
[f for f in dir(qs.plots) if f[0] != '_']
['daily_returns', 'distribution', 'drawdown', 'drawdowns_periods', 'earnings', 'histogram', 'log_returns', 'monthly_heatmap', 'returns', 'rolling_beta', 'rolling_sharpe', 'rolling_sortino', 'rolling_volatility', 'snapshot', 'yearly_returns']
*** Full documenttion coming soon ***
In the meantime, you can get insights as to optional parameters for each method, by using Python's help
method:
help(qs.stats.conditional_value_at_risk)
Help on function conditional_value_at_risk in module quantstats.stats: conditional_value_at_risk(returns, sigma=1, confidence=0.99) calculats the conditional daily value-at-risk (aka expected shortfall) quantifies the amount of tail risk an investment
Installation
Install using pip
:
$ pip install quantstats --upgrade --no-cache-dir
Install using conda
:
$ conda install -c ranaroussi quantstats
Requirements
- Python >= 3.5+
- pandas (tested to work with >=0.24.0)
- numpy >= 1.15.0
- scipy >= 1.2.0
- matplotlib >= 3.0.0
- seaborn >= 0.9.0
- tabulate >= 0.8.0
- yfinance >= 0.1.38
- plotly >= 3.4.1 (optional, for using
plots.to_plotly()
)
Questions?
This is a new library... If you find a bug, please open an issue in this repository.
If you'd like to contribute, a great place to look is the issues marked with help-wanted.
Known Issues
For some reason, I couldn't find a way to tell seaborn not to return the
monthly returns heatmap when instructed to save - so even if you save the plot (by passing savefig={...}
) it will still show the plot.
Legal Stuff
QuantStats is distributed under the Apache Software License. See the LICENSE.txt file in the release for details.
Please drop me a note with any feedback you have.
Ran Aroussi
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